Molecular Targets of Ferroptosis in Hepatocellular Carcinoma.

Ferroptosis is a special form of regulatory cell death caused by the accumulation of intracellular iron and lipid peroxidation. Here, we summarize the research progress on ferroptosis in hepatocellular carcinoma (HCC), trace the development of the concept of ferroptosis and its key regulatory factors, and discuss the application value of ferroptosis in the treatment of HCC from different perspectives. We believe that exploring the relationship between ferroptosis and HCC and clarifying the metabolism and expression of ferroptosis-specific genes and molecules will accelerate the development of novel ferroptosis-related molecules as HCC markers and therapeutic targets. We hope to provide a theoretical basis for better diagnosis and treatment to effectively improve the prognosis of patients with HCC.

Integrated nomogram based on five stage-related genes and TNM stage to predict 1-year recurrence in hepatocellular carcinoma.

BACKGROUND: The primary tumor, regional lymph nodes and distant metastasis (TNM) stage is an independent risk factor for 1-year hepatocellular carcinoma (HCC) recurrence but has insufficient predictive efficiency. We attempt to develop and validate a nomogram to predict 1-year recurrence in HCC and improve the predictive efficiency of the TNM stage. METHODS: A total of 541 HCC patients were enrolled in the study. The risk score (RS) model was established with the logistic least absolute shrinkage and selector operation algorithm. The predictive nomogram was further validated in the internal testing cohort and external validation cohort. The area under the receiver operating characteristic curves (AUCs), decision curves and clinical impact curves were used to evaluate the predictive accuracy and clinical value of the nomogram. RESULTS: In the training cohort, we identified a RS model consisting of five stage-related genes (NUP62, EHMT2, RANBP1, MSH6 and FHL2) for recurrence at 1 year. The 1-year disease-free survival of patients was worse in the high-risk group than in the low-risk group (P < 0.0001), and 1-year recurrence was more likely in the high-risk group (Hazard ratio: 3.199, P < 0.001). The AUC of the nomogram was 0.739, 0.718 and 0.693 in the training, testing and external validation cohort, respectively, and these values were larger than the corresponding AUC of the TNM stage (0.681, 0.688 and 0.616, respectively). CONCLUSIONS: A RS model consisting of five stage-related genes was successfully identified for predicting 1-year HCC recurrence. Then, a novel nomogram based on the RS model and TNM stage to predict 1-year HCC recurrence was also developed and validated.

Integrated Nomograms for Preoperative Prediction of Microvascular Invasion and Lymph Node Metastasis Risk in Hepatocellular Carcinoma Patients.

BACKGROUND: The aim of the present work is to develop and validate accurate preoperative nomograms to predict microvascular invasion (MVI) and lymph node metastasis (LNM) in hepatocellular carcinoma. PATIENTS AND METHODS: A total of 268 patients with resected hepatocellular carcinoma (HCC) were divided into a training set (n = 180), in an earlier period, and a validation set (n = 88), thereafter. Risk factors for MVI and LNM were assessed based on logistic regression. Blood signatures were established using the least absolute shrinkage and selection operator algorithm. Nomograms were constructed by combining risk factors and blood signatures. Performance was evaluated using the training set and validated using the validation set. The clinical values of the nomograms were measured by decision curve analysis. RESULTS: The risk factors for MVI were hepatitis B virus (HBV) DNA loading, portal hypertension, Barcelona liver clinic (BCLC) stage, and three computerized tomography (CT) imaging features, namely tumor number, size, and encapsulation, while only BCLC stage, Child-Pugh classification, and tumor encapsulation were associated with LNM. The nomogram incorporating both risk factors and blood signatures achieved better performance in predicting MVI in the training and validation sets (C-indexes of 0.828 and 0.804) than the LNM nomogram (C-indexes of 0.765 and 0.717). Calibration curves also demonstrated a good fit. The decision curves indicate significant clinical usefulness. CONCLUSIONS: The novel validated nomograms for HCC patients presented herein are noninvasive preoperative tools that can effectively predict the individualized risk of MVI and LNM, and this predictive power can aid doctors in explaining the illness for patient counseling.

Novel nomograms to predict lymph node metastasis and liver metastasis in patients with early colon carcinoma.

BACKGROUND: Lymph node status and liver metastasis (LIM) are important in determining the prognosis of early colon carcinoma. We attempted to develop and validate nomograms to predict lymph node metastasis (LNM) and LIM in patients with early colon carcinoma. METHODS: A total of 32,819 patients who underwent surgery for pT1 or pT2 colon carcinoma were enrolled in the study based on their records in the SEER database. Risk factors for LNM and LIM were assessed based on univariate and multivariate binary logistic regression. The C-index and calibration plots were used to evaluate LNM and LIM model discrimination. The predictive accuracy and clinical values of the nomograms were measured by decision curve analysis. The predictive nomograms were further validated in the internal testing set. RESULTS: The LNM nomogram, consisting of seven features, achieved the same favorable prediction efficacy as the five-feature LIM nomogram. The calibration curves showed perfect agreement between nomogram predictions and actual observations. The decision curves indicated the clinical usefulness of the prediction nomograms. Receiver operating characteristic curves indicated good discrimination in the training set (area under the curve [AUC] = 0.667, 95% CI 0.661-0.673) and the testing set (AUC = 0.658, 95% CI 0.649-0.667) for the LNM nomogram and encouraging performance in the training set (AUC = 0.766, 95% CI 0.760-0.771) and the testing set (AUC = 0.825, 95% CI 0.818-0.832) for the LIM nomogram. CONCLUSION: Novel validated nomograms for patients with early colon carcinoma can effectively predict the individualized risk of LNM and LIM, and this predictive power may help doctors formulate suitable individual treatments.

Comparative Transcriptome Analysis of Temperature-Induced Green Discoloration in Garlic.

Green discoloration is one of the most important problems that cause low quality of product in the processing of garlic, which can be induced by low-temperature stress. But the mechanism of low temperature-induced green discoloration is poorly understood. In the present study, the control garlic and three low temperature-treated garlic samples (stored at 4°C with 10, 15, and 40 days, respectively) were used for genome-wide transcriptome profiling analysis. A total of 49280 garlic unigenes with an average length of 1337 bp were de novo assembled, 20231 of which were achieved for functional annotation. When being suffered from 10, 15, and 40 days of low-temperature treatment, an increased degree of discoloration was observed, and a total of 4757, 4401, and 2034 unigenes showed a differential expression, respectively. Finally, 5923 differentially expressed genes (DEGs) were found to respond to the low-temperature stress, of which 3921 were identified in at least two treatments. Among these stress-responsive unigenes, there were large numbers of enzyme-encoding genes, which significantly enriched the pathway "proteasome," many genes of which are potentially involved in the garlic discoloration, such as 7 alliinase-encoding genes, 5 γ-glutamyltranspeptidase-encoding genes, and 1 δ-aminolevulinic acid dehydratase-encoding gene. These stress-responsive enzyme-encoding genes are possibly responsible for the low-temperature-induced garlic discoloration. The identification of large numbers of DEGs provides a basis for further elucidating the mechanism of low-temperature-induced green discoloration in garlic.